Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

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Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

Journal of Aerospace Computing Information and Communication ◽

10.2514/1.36917 ◽

2009 ◽

Vol 6 (4) ◽

pp. 271-290 ◽

Cited By ~ 64

Author(s):

Jung Leng Foo ◽

Jared Knutzon ◽

Vijay Kalivarapu ◽

James Oliver ◽

Eliot Winer

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Unmanned Aerial Vehicles ◽

Particle Swarm ◽

Swarm Optimization ◽

B Splines ◽

Aerial Vehicles

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Pareto Optimal PID Tuning for Px4-Based Unmanned Aerial Vehicles by Using a Multi-Objective Particle Swarm Optimization Algorithm

Aerospace ◽

10.3390/aerospace7060071 ◽

2020 ◽

Vol 7 (6) ◽

pp. 71

Author(s):

Victor Gomez ◽

Nicolas Gomez ◽

Jorge Rodas ◽

Enrique Paiva ◽

Maarouf Saad ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Unmanned Aerial Vehicles ◽

Particle Swarm ◽

Swarm Optimization ◽

Multi Objective ◽

Pid Tuning ◽

Aerial Vehicles ◽

The Stability ◽

Selection Of

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study.

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Particle Swarm Optimization for collision-free 4D trajectory planning in Unmanned Aerial Vehicles

2013 International Conference on Unmanned Aircraft Systems (ICUAS) ◽

10.1109/icuas.2013.6564702 ◽

2013 ◽

Cited By ~ 17

Author(s):

D. Alejo ◽

J. A. Cobano ◽

G. Heredia ◽

A. Ollero

Keyword(s):

Particle Swarm Optimization ◽

Unmanned Aerial Vehicles ◽

Trajectory Planning ◽

Particle Swarm ◽

Swarm Optimization ◽

Aerial Vehicles

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Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

Applied Sciences ◽

10.3390/app11083417 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3417

Author(s):

Nafis Ahmed ◽

Chaitali J. Pawase ◽

KyungHi Chang

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Unmanned Aerial Vehicles ◽

Fitness Function ◽

Particle Swarm ◽

Pso Algorithm ◽

Human Beings ◽

Swarm Optimization ◽

Aerial Vehicles ◽

Area Surveillance

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

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